TWI672665B - Green building efficiency simulation and analysis system and optimal decision method - Google Patents

Abstract

The invention provides a green building performance simulation analysis system, and sets an energy saving target through A. B. acquires meteorological data; C. internal setting; D. executes energy saving computing module; E. visual performance analysis and hotspot tracking; F. evaluation And the program correction; and G. Select the optimization plan and other operational steps, from the beginning of the design with the Building Information Model (BIM) as the basic tool, in accordance with the geochemical climatic conditions, the building performance analysis (BPA), through the design And the “analytical” decision cycle, using the building electricity intensity (EUI) as the unit of measure for the comprehensive performance index of energy consumption, and using the “optimized performance percentage” as the condition for rating, continuously optimizing the design to produce environmentally beneficial optimization. The plan finally achieves the goal of pursuing environmental sustainability.

Description

 Green building efficiency simulation analysis system and its optimal decision-making method  

The invention relates to a green building simulation analysis system, in particular to a green building efficiency simulation analysis system capable of adapting the decision-making method of energy saving and carbon reduction design according to the climatic conditions in the localization.

The purpose of the building simulation is to analyze the architectural design or information provided, and to further modify the design or project through the results of the building simulation. Therefore, the building front-end operation process will be continuously circulated through information collection, simulation and analysis. After that, the collegiate result is obtained to be able to carry out subsequent related operations. Information collection, simulation, and analysis are usually divided into three different processing systems, resulting in a large number of repetitive modeling and setting parameters, and lack of efficiency.

In the prior art, the information collection part will be different depending on the plan or design purpose, and the building simulation part is based on Building infoimation modeling (BIM), which will construct information and parameters. The time and other data are included in the 3D model components. Compared with the previous plane-based Computer Aided Architectural Design (CAAD), the differences include (1) changing from the planar 2D linear thinking mode to the 3D stereoscopic Visual simulation to 4D time management, (2) from drawing operations to digital information management, and (3) from static single operations to dynamic linking. The analysis part is based on Building Performance Analysis (BPA), which uses computer software to predict building performance, and outputs and visualizes simulated images, data, statistical analysis charts and forms, providing visual visualization of building performance. Data analysis results to assist users in understanding the performance of their design solutions and as a basis for design decisions or as a continuous optimization design. According to statistics, the variety of BIM systems on the market and about 350 BPA analysis systems make communication and message transmission between BIM and various BPAs difficult. Therefore, the selection and integration of software tools is extremely important.

However, under the climatic environment and the global energy crisis, how to apply BIM tools to obtain more environmentally-friendly architectural design has become an important issue in the construction and construction-related industries in recent years. Therefore, the green building information model (Green BIM) was derived, emphasizing the combination of BIM and BPA software technology, and integrated design to promote architectural design, analysis, and reasonable decision-making cycle, and then obtain more environmentally-friendly optimization development. However, the implementation of the Green Building Information Model (Green BIM) is not an easy task. It has the following difficulties to overcome: 1. The selection and integration of software tools; 2. The establishment of integrated design procedures and optimization conditions; and 3. The past regional meteorology. Data acquisition is not easy.

Therefore, in order to solve the above problems, the present invention provides a green building performance simulation analysis system comprising: an input device, provided with a processor; a modeling module connected to the processor to generate a volume model; a database, The utility model comprises a meteorological data database and a geographical environment database connected to the modeling module wirelessly or by wire; a performance analysis module is connected with the processor and the modeling module to generate an energy analysis And an energy-saving computing module for generating a visual analysis and calculating an optimized performance percentage, and connecting to the modeling module, the performance analysis module, and the processor.

The modeling module is based on Building Information Modeling (BIM), which mainly includes receiving, simulating and outputting geometric, physical and topological information, and generating the quantitative model of the building. The performance analysis module is based on Building Performance Analysis (BPA). The main performance analysis items include building sunshine and lighting, indoor lighting, shading and shadowing, shading optimization, heat radiation, air and convection, and air conditioning consumption. Energy, sound design, ventilation environment, visual impact, overall building energy performance simulation and life cycle energy consumption and carbon emissions, etc., provide analytical data information, and convert the volume model into the energy analysis model.

The geographic environment database of the database includes data and image information of terrain, roads and building spaces; the weather data database includes meteorological data of a real weather station and a virtual weather station, wherein the real weather The data format of the station and the virtual weather station is the International Meteorological Year (TMY). Due to this technical breakthrough of the virtual weather station, the invention is not limited to the application of the area.

The input device is a personal computer, a tablet computer or a smart phone. The invention may further comprise an output device coupled to the processor, wherein the output device is a printer, display or projector.

In addition, the present invention may further include a third-party use module, and the processor and the energy-saving computing module are connected, so that other users can quickly add other simulation modules, and the data can be output as gbXML (Green Building XML). The building simulation format is uploaded to the energy-saving computing module for energy analysis, or the simulation analysis result of the computing module is output in a gbXML (Green Building XML) building simulation format to the third-party usage model capable of adding a new function. group.

The visual analysis generated by the energy-saving computing module includes a base climatic condition analysis, a building energy usage performance analysis, and a building physical environment analysis. The base climate analysis includes meteorological station's typical meteorological year (TMY) weather data and wind environment analysis; the building energy use performance analysis includes electricity density (EUI), building life cycle energy and cost calculations, and energy recovery/energy conservation. Potential; the physical environment analysis of the building includes average carbon emissions, monthly air conditioning loads, and peak demand for electricity.

The invention adopts the optimization performance percentage as the condition of rating, and uses the electric density (EUI) as a unit of measurement of the comprehensive energy consumption integral index of the building, and uses the energy use intensity (EUI) as the annual power consumption per unit area of the building. % of the optimized performance = (electrical density value for the reference scheme - electrical density value for the optimization scheme / electrical density value for the reference scheme) × 100%.

It can be seen from the above that the green building performance simulation analysis system of the present invention can simulate a building and perform building performance analysis, and provide the modeling module, the performance analysis module and the third party using the module. The geometric and non-geometric information and meteorological data are uploaded to the energy-saving computing module in the gbXML (Green Building XML) building simulation format for energy analysis, and the building power intensity (EUI) is used as the unit of measurement for the comprehensive performance index of energy consumption. And using the optimized performance percentage as a condition of the rating, returning the result of the visual analysis of the building performance value, and tracking the hotspot from the result of the visual analysis to find the cause of the large energy consumption, as a follow-up solution correction Basis.

Another object of the present invention is to provide an optimization decision-making method for a green building performance simulation analysis system, the steps of which include: A. setting an energy-saving goal: setting an energy-saving target and using an optimized performance percentage as a rating condition; B. obtaining meteorological data: obtaining Data from a real weather station and a virtual weather station; C. Internal setting: internal performance parameter setting for a modeling module and a performance analysis module, generating a quantitative model of the reference scheme and an energy analysis model D. Execute the energy-saving computing module: the geometric model, the non-geometric information and the meteorological data of the energy model are uploaded to an energy-saving computing module in the gbXML (Green Building XML) building simulation format. Energy consumption analysis; E. Visual performance analysis and hotspot tracking: After the energy-saving calculation module is calculated, a visual analysis is sent back, and hotspot tracking is performed to find out the factors that affect energy consumption; F. Evaluation and solution correction: According to the results of the visual analysis and hotspot tracking, adjust the control energy consumption factor, propose a correction scheme; and G. select Optimization scheme: The correction scheme of the energy saving target that reaches the set percentage of the optimization performance is taken as a candidate optimization scheme, and the correction is continued until the end of the decision cycle process to obtain an optimization scheme.

If the modification scheme does not reach the set energy saving target of the optimized performance percentage, the process returns to step C and repeats the operation steps C~G.

The percentage of the optimized performance is (the electrical density value of the reference scheme - the electrical density value of the optimization scheme / the electrical density value of the optimization scheme) × 100%. The data format of the real weather station and the virtual weather station in step B is a typical meteorological year internationally. The performance parameters in step C include building type, activity type and user density, shell properties, air conditioning and lighting. The visual analysis in step E includes a base climatic condition analysis, a building energy usage performance analysis, and a building physical environment analysis, wherein the base climatic condition analysis includes weather station typical meteorological weather data and wind environment analysis; the building energy use Performance analysis includes electricity density (EUI), building life cycle energy and cost calculations, and energy recovery/energy savings potential; the building's physical environment analysis includes average carbon emissions, monthly air conditioning loads, and peak demand.

It can be seen from the above that the method for optimizing the decision of the system of the present invention, the modeling module and the performance analysis module have the ability to fully verify the integrated design, analyze the decision cycle and design optimization, and use the simulation software to calculate the building. "The energy analysis of the stage, the performance analysis and optimization design decision cycle occurs in the early design stage (including the Schematic Design (SD) stage and the Design Development (DD) stage), and uses the electricity density ( EUI) As the unit of the comprehensive energy consumption index of the building, according to the set optimization performance percentage percentage evaluation conditions, in the preliminary design stage, the “concept quantity body” is used to find the optimization scheme of different configuration modes; then enter the detailed design stage and join the detailed construction stage. Elements, adjusting the attributes and parameters of the elements, so that performance continues to achieve an optimal solution.

10‧‧‧Input device

11‧‧‧ Processor

20‧‧‧Modeling module

30‧‧‧ Performance Analysis Module

40‧‧‧ Third party use module

50‧‧‧Energy Saving Computing Module

60‧‧‧ Output device

70‧‧‧Database

71‧‧‧Weather Data Database

72‧‧‧Geographic Database

1 is a schematic structural view of a green building performance simulation analysis system according to the present invention; FIG. 2 is a flow chart of an optimalization decision method for a green building performance simulation analysis system of the present invention; FIG. 3 is a schematic diagram of a quantitative model of the present invention; FIG. 5 is a schematic diagram of an internal setting of the present invention; FIG. 6 is a schematic diagram of a wind-up diagram of the present invention; FIG. 7 is a schematic diagram of a power consumption ratio analysis of the present invention; FIG. 9( a ) is a schematic diagram of a reference scheme according to an embodiment of the present invention; FIG. 9( b ) is a schematic diagram of a modification scheme 1 according to an embodiment of the present invention; FIG. 9( c ) FIG. 9(d) is a schematic diagram of a modification scheme 3 of an embodiment of the present invention; FIG. 10 is a schematic diagram of a detail of an architectural element added to the present invention; A schematic diagram of the addition of another detailed architectural element of the present invention.

Please refer to FIG. 1. FIG. 1 is a schematic structural diagram of a green building performance simulation analysis system according to the present invention. The present invention provides a green building performance simulation analysis system, comprising: an input device 10 provided with a processor 11; a modeling module 20 connected to the processor 11; a database 70 comprising a weather data database 71 and a geographic environment database 72, which can be connected to the modeling module 20; a performance analysis module 30 connected to the processor 11 and the modeling module 20; and an energy-saving computing module 50, The modeling module 20, the performance analysis module 30, and the processor 11 are connected.

The input device 10 is a personal computer, a tablet computer, or a smart phone. The invention may further comprise an output device 60 coupled to the processor 11, the output device 60 being a display, printer or projector.

Please refer to FIG. 1 and FIG. 3. FIG. 3 is a schematic diagram of a measurement model according to the present invention. The modeling module 20 is based on Building Information Modeling (BIM) and mainly includes receiving, simulating and outputting geometric, physical and topological information for generating a three-dimensional building-body model, as shown in FIG. As shown, the volume model is a digital model that records the geometric spatial relationships of buildings, geographic information, the number of building elements, and related properties. In addition to establishing 3D geometric information, the modeling module 20 also includes some non-geometric information that needs to be transmitted to the performance analysis module 30.

Please refer to FIG. 1 and FIG. 4. FIG. 4 is a schematic diagram of an energy analysis model of the present invention. The performance analysis module 30 is based on Building Performance Analysis (BPA). The main items may include building sunshine and daylighting, indoor lighting, shading and shadow analysis, shading optimization, heat radiation, air and convection, and air conditioning energy consumption. , sound design, ventilation environment, visual impact, overall building energy performance simulation and life cycle energy consumption and carbon emissions analysis, provide analytical data information, and used to generate an energy analysis model, as shown in Figure 4.

The energy-saving calculation module 50 performs energy consumption analysis, generates a visual analysis and calculates an optimized performance percentage, the visual analysis includes a base climatic condition analysis, a building energy usage performance analysis, and a building physical environment analysis, and the base climatic condition analysis Includes typical meteorological weather data for weather stations and wind environment analysis; the building energy use performance analysis includes electricity density (EUI), building life cycle energy and cost calculations, and energy recovery/energy saving potential; the building physical environment analysis includes average Carbon emissions, monthly air conditioning loads, and peak demand for electricity. The percentage of optimized performance = (the electrical density of the reference scheme - the electrical density value of the optimization scheme / the electrical density value of the optimization scheme) × 100%, and the electrical density (EUI) is used as a unit of measurement of the comprehensive index of the overall energy consumption of the building, and The percentage of the optimized performance is calculated by using the energy use intensity (EUI) as the annual power consumption per unit area of the building.

The meteorological data database 71 includes data from a real weather station and a virtual weather station. The format of the data source is the internationally typical typical weather year (TMY), that is, each weather station is based on the monthly average of nearly 30 years. And from the data of the past 10 years, the average value of nearly 30 years in each month is selected as the typical meteorological year. Based on the TMY data of each real weather station, the simulation operation of the virtual weather station is carried out to make up the data gap between the actual stations, and the meteorological grid distance within 14 km can be improved to improve the simulation accuracy. . Due to technological breakthroughs in virtual weather stations, the invention is not limited to the application of the area. The geographic environment database 72 contains data and visualization information of terrain, roads, and building spaces. The database 70 can also be a cloud database that is wirelessly connected to the modeling module 20 via a network or wifi.

The present invention focuses on the early design stage of preliminary design (SD) to detail design (DD), and has the ability to fully verify integrated design, analyze decision cycle and design optimization. However, if further optimization is required, for example, regeneration is added. The simulation analysis of the operation of the energy device may further include a third-party use module 40, and the processor 11 and the energy-saving computing module 50 are connected, and the data analyzed by the third-party using the module 40 may be output as gbXML (Green). The Building XML) building simulation format is uploaded to the energy-saving computing module 50 for energy consumption analysis, or the simulation result of the computing module 50 is output to the gbXML (Green Building XML) building simulation format to the first one capable of adding a new function. The three parties use the module 40 so that other users can quickly add other simulation modules.

In the embodiment, the modeling module 20 and the performance analysis module 30 are respectively an architectural design application software and a building performance analysis software, for example, using Autodesk Revit as a BIM tool and Energy Analysis for Revit as a BPA tool. Energy Analysis for Revit is a BPA tool integrated with Revit. It has an interface that is friendly to architects and designers. The energy-saving computing module 50 can be a software for building energy analysis, such as DOE-2. The building energy consumption simulation engine performs energy analysis.

Inputting property parameters of the building from the input device 10, such as: building type, activity type and user density, shell properties (such as construction material, heat transfer coefficient or heat insulation coefficient), air conditioning and lighting, etc., through the processor 11 to The modeling module 20 loads the selected graphic data, geographic environment and meteorological data from the database 70, and imports the base image for volume modeling to obtain gbXML (Green Building XML) The building simulation format is provided to the performance analysis module 30 for subsequent correlation analysis, and the performance analysis module 30 provides analysis data information and converts the energy analysis model into energy analysis modules 50 for energy consumption analysis. Returning the visual performance analysis, calculating the optimization performance percentage as a condition of rating, and performing hotspot tracking to find out the cause of the large energy consumption, and also the modeling module 20, the performance analysis module 30, and the The 3D model and analysis results generated by the energy-saving computing module 50 are presented or passed to the output device 60 for presentation or printing.

It can be seen from the above that the present invention uses the simulation software to calculate the energy consumption analysis of the building operation use stage, and the performance analysis and optimization design decision cycle occurs in the early design stage (including: preliminary design stage, detailed design stage), and the construction is completed. The module, the performance analysis module, and the geometric and non-geometric information and meteorological data provided by the third-party module are uploaded to the energy-saving computing module in the gbXML building simulation format for energy analysis and returned to the building. The results of the visual analysis of the performance values, and the building electrical strength (EUI) as the unit of measurement of the comprehensive performance index of the energy consumption, using the optimization performance percentage as the condition of the rating, from the results of the visual analysis, tracking and judging the hotspot The factors that affect the large energy consumption are used as the basis for the revision of the follow-up plan to produce an optimal solution that meets environmental benefits.

Therefore, the present invention considers the fit of the BIM software, the virtual weather station technology and the energy consumption analysis engine conform to the standard test, overcoming the selection and integration of the software tool; Second, the energy-saving computing module adopts the DOE-2 engine precision. It has considerable credibility; and third, the problem of obtaining regional meteorological data in the past is not easy.

Please refer to FIG. 2. FIG. 2 is a flow chart of an optimal decision-making method for the green building performance simulation analysis system of the present invention. The steps include: A. setting energy saving goals; B. acquiring meteorological data; C. internal setting; D. executing energy saving computing module; E. visual performance analysis and hotspot tracking; F. evaluation and program correction; and G. selection optimization Program. The steps are detailed below:

Please refer to Figure 2 and Table 1. Table 1 shows the electrical density (EUI) statistics of each building area. Step A. Set the energy saving goal: set the energy saving target and use a percentage of optimized performance as the rating condition. The use of electrical density (EUI) as a unit of measurement for the overall performance of building energy consumption, the use of energy density (EUI) for the annual electricity consumption per unit area of the building, and with reference to various types issued by the Energy Bureau of the Ministry of Economic Affairs The electrical unit density (EUI) statistical table of the building area is shown in Table 1 below. The classification and category of the building use corresponding to the project are identified. The “average value” is used as the “common benchmark” for reference to the “project design”. The optimized performance percentage is used as a target setting or rating condition, and the optimized performance percentage = (the reference scheme uses the electric density - the optimization scheme uses the electric density value / the reference scheme uses the electric density) × 100%. For example, the energy saving target of the preliminary design phase (SD) is set to; the percentage of optimized performance is expected to increase by more than 21% of the “baseline plan”, and the detail design phase (DD) is intended to increase the percentage of optimized performance by 3%.

The benchmarks for the above energy-saving targets are not irreplaceable. Other feasible energy-saving targets include: (1) Energy cost budget: LEED-certified building energy-saving benchmark, LEED is a green building scoring certification established by the US Green Building Council The system is used to assess whether the building performance can be consistent with sustainability. The benchmark solution inputs the variable parameters specified in ASHRAE 90.1, applies the Energy Plus software simulation output ECB (Energy Cost Budget), and takes the percentage of optimized performance as the threshold for LEED rating. . (2) Net zero energy consumption standard: The power density generated by the annual renewable energy equipment of the design scheme should conform to the annual energy consumption density (EUI) of the building. (3) Passive house standard: The upper limit of energy consumption of air-conditioning equipment in passive houses in units of electric density (EUI). (4) Peak air conditioning energy consumption: The energy consumption of the mechanical system must not exceed the peak load. (5) Carbon footprint: The carbon rejection coefficient of electricity can be converted from the electric load into the carbon emission during the use phase of the building. The benchmarks for the above energy-saving targets, the ideals and purposes of the visual design project, and the benchmarks for other energy targets, the feasibility of adoption depends on the maturity of the government's expectations to lead the industry and technical feasibility.

Step B. Obtain meteorological data: Acquire data including from a real weather station and a virtual weather station. The format of the data source is the International General Meteorological Year (TMY), which is based on the monthly average of nearly 30 years, and the average of nearly 30 years from each year. Value as a typical meteorological year. Based on the data of the Typical Meteorological Year (TMY) of each real weather station, the simulation operation of the virtual weather station is performed to make up the data gap between the actual stations and the meteorological grid distance. Increase the simulation accuracy within 14 km. In the past, limited by regional meteorological data, its application was hindered. However, the virtual weather station technology breakthrough based on cloud computing makes Green BIM not limited to regional applications. For example, taking the base in Beibei District of Taichung as an example, setting the project and inputting the latitude and longitude of the base location can return the TMY weather data of the nearest weather station.

Please refer to FIG. 2, FIG. 3, FIG. 4 and FIG. 5. FIG. 5 is a schematic diagram of the internal setting of the present invention. Step C. Internal setting: internal performance parameter setting for a modeling module and a performance analysis module, generating a quantitative model of the reference scheme and an energy analysis model. The first is the model construction of the initial scheme. The internal performance parameters include building type, activity type and user density, shell properties (such as construction material, heat transfer coefficient or heat insulation coefficient), air conditioning and lighting, as shown in Figure 3. Simple modeling in Revit, first establish the volume and floor, the building type is selected as the hotel building, the window opening rate is set to 40%, the building detailed operation table 12/7 facilities (representing the weekly operating hours of the building operation 12/ 7,12/7 represents seven days of operation per week, 12 hours of operation per day), external air volume information, and internal performance parameter settings such as the HVAC system. As shown in Figure 5, the heat-through air-conditioning (HAVC) system refers to the settings selected for the electromechanical air-conditioning system. The preset options are central VAV, hot water heating, freezer 5.96 COP, boiler 84.5 efficiency; external air volume information, Refers to setting the amount of air required by each person, the amount of ventilation per unit area, and the number of air changes per hour. The other construction materials use the default values of the BIM model. After the setting is completed, the model is converted into an energy analysis model, as shown in Figure 4.

Step D. Execute the energy-saving calculation module: the geometric model, the non-geometric information and the meteorological data of the energy analysis model are uploaded to an energy-saving computing module in a gbXML format, such as a network-based GreenBuilding Studio. The DOE-2 simulation engine of the energy analysis software performs energy analysis.

Step E. Visual performance analysis and hotspot tracking: After the energy-saving calculation module is operated, a visual analysis is returned, and hotspot tracking is performed to find out the factors that affect the energy consumption. The visual analysis includes a base climatic condition analysis, a building energy usage performance analysis, and a building physical environment analysis, and the analysis results can be used as a reference for subsequent program revisions. The climatic condition analysis of the base includes the wind environment analysis in addition to the TMY weather data of the weather station provided in step B, as shown in FIG. 6, and FIG. 6 is a schematic diagram of the wind rose diagram of the present invention. The building's energy use analysis and the building's physical environment analysis include electricity density (EUI), building life cycle (30 years) energy and cost calculations, energy recovery / energy saving potential, average carbon emissions, monthly air conditioning load, peak electricity use Requirements, etc., can analyze the performance calculation results on the target setting content, and find out the key factor feedback revision.

Please refer to Table 2, FIG. 7 and FIG. 8. Table 2 is a table of results analysis results of the reference scheme of the present invention; FIG. 7 is a schematic diagram of power consumption ratio analysis of the present invention; and FIG. 8 is a schematic diagram of monthly energy load analysis of the present invention. Due to the different energy consumption characteristics of various types of buildings, the evaluation of EUI should also be meaningful by comparison with “architectural buildings”. According to the calculation simulation results, the initial scheme has an electricity density (EUI) value of 204 kWh/m ² . Yr, as shown in Table 2 below, compares the electricity density (EUI) statistics of various types of buildings published by the Energy Bureau of the Ministry of Economic Affairs in Table 1, which is higher than the average electricity consumption of the general hotel by 195.6 kWh/m ² . Yr, but lower than the maximum electricity consumption of 223.6kWh/m ² . Yr, so the initial program EUI value is still within a reasonable range and can be used as a benchmark.

After that, hotspot tracking is performed to find out the factors that affect the energy consumption. For example, according to the power consumption ratio analysis in Figure 7, it can be seen that the energy-consuming equipment has the highest air-conditioner 46%, followed by the lighting 21%. According to the monthly energy load analysis in Figure 8, the energy load composition analysis shows that the solar radiation and window heat conduction are the largest sources of air conditioning burden, followed by the solar radiation caused by lighting equipment and window opening; In terms of electricity consumption distribution, electricity consumption is higher in July and August in summer, so we should focus on the improvement of summer electricity consumption to reduce the operational burden.

Step F. Evaluation and solution correction: According to the results of the visual analysis and hotspot tracking, adjust the control energy consumption factor and propose a correction plan. According to the wind-up diagram of Fig. 6, it can be seen that in the present embodiment, the summer monsoon of the base is the most frequent in the westward direction and the westward direction, and the winter monsoon is the most frequent in the northeast. In order to make the open space of the designed building windy in summer and shelter from the wind in winter, in the preliminary design (SD) stage, three correction schemes were made to adjust the relationship between the building volume and the outdoor space, as shown in Figure 9(a)~ 9(a) is a schematic diagram of a reference scheme according to an embodiment of the present invention; and FIG. 9(b) is a schematic diagram of a modification scheme 1 according to an embodiment of the present invention; FIG. 9(c) A schematic diagram of a second modification of an embodiment of the present invention; and FIG. 9(d) is a schematic diagram of a third modification of an embodiment of the present invention.

Step G. Selecting an optimization scheme: a correction scheme of the energy saving target that reaches the set percentage of the optimization performance is used as a candidate optimization scheme, and the correction is continued until the end of the decision cycle process to obtain an optimization scheme. As shown in Fig. 9(a) to Fig. 9(d), the reference scheme EUI value is 204 kWh/m ² . Yr, correction scheme one EUI value is 201kWh/m ² . Yr, correction scheme 2 EUI value is 199kWh/m ² . Yr, the correction scheme three EUI value is 153kWh/m ² . The percentage of optimized performance of yr is 1.47%, 2.45%, and 25%, respectively. It can be seen that only the modified scheme 3 meets the requirement of improving the performance percentage target of 21% or more of the benchmark scheme, and is a candidate optimization scheme.

If the correction scheme does not meet the set requirements of the optimized performance percentage target, the operation steps C~G are repeated, and the evaluation is performed until the set energy saving target is satisfied.

It can be seen from the above that in the conceptual design stage (SD), the present invention finds the concept quantity of the candidate optimization, based on the concept quantity of the candidate optimization, enters the detailed design stage (DD), returns to the Revit modeling, and repeats the operation. Step C~G, continuous correction until the end of the decision cycle process to get the optimization plan. According to the visual analysis of the return, the hotspot tracking is carried out to find out the factors that affect the energy consumption. As shown in Figure 8, the monthly energy load analysis analyzes the heat conduction of the window and the external wall as the largest source of air conditioning burden. Please refer to FIG. 10 and FIG. 11. FIG. 10 is a schematic view showing the addition of a detailed architectural element of the present invention, and FIG. 11 is a schematic view showing the addition of another detailed architectural element according to the present invention, as shown in FIG. 10 and FIG. , the window opening rate is reduced from 40% to 29%, the outer wall is set as the heat insulation wall, the window is set as double-layer low-radiation glass, and the horizontal concealer is added to the south and north elevations, and the east and west facades are added. Add vertical concealer to get the optimal solution, the EUI value is 140kWh/m2. Yr, which is different from the benchmark scheme by 64kWh/m2. Yr, calculated the percentage of optimized performance is 31%.

It can be seen from the above that the method for optimizing the decision of the system of the present invention, the modeling module and the performance analysis module have the ability to fully verify the integrated design, analyze the decision cycle and design optimization, and use the simulation software to calculate the building. "The energy analysis of the stage, the performance analysis and optimization design decision cycle occurs in the early design stage (including the Schematic Design (SD) stage and the Design Development (DD) stage), and uses the electricity density ( EUI) As the unit of the comprehensive energy consumption index of the building, according to the set optimization performance percentage percentage evaluation conditions, in the preliminary design stage, the “concept quantity body” is used to find the optimization scheme of different configuration modes; then enter the detailed design stage and join the detailed construction stage. Elements, adjusting the attributes and parameters of the elements, so that performance continues to obtain an optimal solution, gaining the accuracy of its simulation analysis prediction.

Therefore, due to the consideration of the convergence of the invention and the BIM software, the virtual weather station technology and the energy consumption analysis engine comply with the standard test, and the optimal decision-making method, overcoming the selection and integration of software tools; Second, the integrated design procedure And the establishment of optimization conditions; and third, the past regional meteorological data to obtain difficult problems.

Claims (6)

An optimization decision-making method for a green building performance simulation analysis system includes the following steps: a preliminary design stage, at least one correction scheme of the building is proposed by adjusting a relationship between the building volume and the outdoor space, the preliminary design stage includes :A. Setting energy-saving goals: setting an energy-saving target for the building, where the energy-saving goal is a rating condition with an optimized performance percentage; B. acquiring external meteorological data: including data from a real weather station and a virtual weather station C. Internal setting: input an attribute parameter of the building to a modeling module, and the modeling module generates a volume model according to the attribute parameter and the external meteorological data, and the performance analysis module according to the The volume model is analyzed and converted into an energy analysis model, wherein the attribute parameters include building type, activity type and user density, shell properties, air conditioning, and lighting; D. executing energy-saving computing module: the volume model and the Geometry, non-geometric information and meteorological data for energy analysis models, together with gbXML (Green Building XM) L) The building simulation format is uploaded to an energy-saving computing module for energy analysis, and a visual performance analysis is returned, and the percentage of the optimized performance is calculated as a rating condition, wherein the optimization performance percentage is calculated as Density value - the electric density value of the optimization scheme) / the electric density value of the reference scheme × 100%; E. Visual analysis and hotspot tracking: According to the visual performance analysis, the hotspot tracking is performed to find out the factors that affect the energy consumption of the building. ;F.Evaluate and propose a correction plan: according to the results of the visual performance analysis and the hotspot tracking, adjust and control the variable energy consumption of the building, and propose the at least one correction plan; G. Evaluation correction scheme: a correction scheme that achieves the energy saving target set by the building is used as a candidate optimization scheme; and a detailed design phase, by adjusting the attribute parameter of the building and adding at least one detailed architectural element to obtain one The optimization scheme includes: H. selecting an optimization scheme: if the correction scheme of the energy saving target is not reached, repeating the operation step CG until each correction scheme reaches the set energy saving target, and then screening out The candidate optimization scheme with the best energy saving effect is the optimization scheme, and the optimization scheme meets the energy saving target; wherein adding the at least one detailed architectural element includes adding a window component, adding a horizontal concealer or adding a vertical concealer; Adjusting the attribute parameter includes the outer wall being set as the heat insulating wall, or the window element being set as a double-layer low-emissivity glass; wherein, in step D, the step of performing a simulation analysis of the operation of the regenerative energy device may be performed: using a third party The data output of the module analysis is uploaded to the energy-saving computing module in the gbXML (Green Building XML) building simulation format. Energy analysis carried out, or outputs the results of a simulation module to calculate energy gbXML (Green Building XML) format to a building simulation ability to add new features to a party access module. The method for optimizing the decision according to claim 1, wherein the data format of the real weather station and the virtual weather station in step B is a typical meteorological year internationally. The method for optimizing the decision as described in claim 1, wherein the visual analysis comprises a base climatic condition analysis, a building energy usage performance analysis, and a building physical environment analysis. The method for optimizing the decision as described in claim 3, wherein the base weather condition analysis comprises weather data of a typical weather year of the weather station and wind environment analysis. The method of optimizing the decision as described in claim 3, wherein the building energy usage performance analysis comprises electricity density (EUI), building life cycle energy consumption and cost calculation, and energy recovery/energy saving potential. The method of optimizing the decision as described in claim 3, wherein the physical environment analysis of the building comprises an average carbon emission, a monthly air conditioning load, and a peak demand for electricity.